2D QSAR, Design, ADMET Prediction, and Docking Study of Novel Coumarin Derivatives as α-Glucosidase Inhibitors

Abstract

A new series of α-glucosidase inhibitors was the subject of investigations using molecular modeling, such as molecular docking, 2D-QSAR, and ADMET prediction. The aim is to obtain new α-glucosidase inhibitors with high activities. The 2D-QSAR model generated was obtained using descriptors from the MOE software. The best model, utilizing the multiple linear regression (MLR) method, yielded 0.91 for the determination coefficient (r2) and 0.10 for the root-mean-square error (RMSE). The developed model’s predictive power was assessed using both internal and external validations, which yielded the Q2 and R2test values of 0.80 and 0.81, respectively. Using the ADMET, it was possible to assess a compound's oral activity prediction. Additionally, molecular docking was used to test the affinity of the ligands (coumarin derivatives with an oxime ester) to the α-glucosidase receptor. Finally, this study may open the door to the creation of coumarin compounds that can inhibit the α-glucosidase enzyme.